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ReadFPLData.py
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ReadFPLData.py
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import pandas as pd
import numpy as np
import os
def ReadPlayerData(path, year, shift_cols, keep_cols):
'''Browse all player folders in a given path and return gameweek data for each player in the season.
Parameters:
path (string) - path where the all player folders for the given season are located
playerID (DataFrame) - maps Player names with FPL ID
understatData (DataFrame) - understat data for all players in a given season
Returns:
playerSeasonData (DataFrame) - Contains data for each gameweek for all players in a given season'''
players = os.listdir(path)
playerSeasonData_df = pd.DataFrame()
prevYear = str(year-1) + '/' + str(year)[2:]
for player in players:
if os.path.isfile(path + player + "/gw.csv"):
playerPath = path + player + "/gw.csv"
df = pd.read_csv(playerPath)
#Do not add players who haven't played at all the entire season (remove =0 for only training Data)
if(df['minutes'].sum()>=0):
df['label'] = df['total_points']
df[shift_cols] = df[shift_cols].shift(1)
if os.path.isfile(path + player + "/history.csv"):
historyPath = path + player + "/history.csv"
history_df = pd.read_csv(historyPath)
history_df = history_df.loc[history_df.season_name == prevYear]
history_df = history_df.reset_index()
history_df = history_df[shift_cols]
history_df = history_df.apply(pd.to_numeric)
if not history_df.empty:
matches = history_df.loc[0,'minutes']/90
df.loc[0, shift_cols] = history_df.loc[0,shift_cols]/matches
playerSeasonData_df = pd.concat([playerSeasonData_df,df], ignore_index=True)
playerSeasonData_df = playerSeasonData_df[keep_cols]
return playerSeasonData_df
def MapPlayerIDtoNameAndTeam(path, playerData_df):
playerRaw_df = pd.read_csv(path + "players_raw.csv")
playerRaw_df['name'] = playerRaw_df['first_name'] + " " + playerRaw_df['second_name']
playerRaw_df['id'] = playerRaw_df.id.astype(int)
playerData_df['player_name'] = playerData_df.element.map(playerRaw_df.set_index('id').name)
playerData_df['web_name'] = playerData_df.element.map(playerRaw_df.set_index('id').web_name)
playerData_df['element_type'] = playerData_df.element.map(playerRaw_df.set_index('id').element_type)
#Map player to player team
teams_df = pd.read_csv(path + "teams.csv" )
playerData_df['player_team'] = playerData_df.element.map(playerRaw_df.set_index('id').team)
playerData_df['player_team'] = playerData_df.player_team.map(teams_df.set_index('id').name)
playerData_df['opponent_team'] = playerData_df.opponent_team.map(teams_df.set_index('id').name)
return playerData_df
def ReadPlayerHistory(path, year, keepCols):
players = os.listdir(path)
playerHistory_df = pd.DataFrame()
prevYear = str(year-1) + '/' + str(year)[2:]
for player in players:
if os.path.isfile(path + player + "/history.csv"):
historyPath = path + player + "/history.csv"
history_df = pd.read_csv(historyPath)
history_df = history_df.loc[history_df.season_name == prevYear]
if not history_df.empty:
history_df = history_df.reset_index()
elementCode = history_df.loc[0,'element_code']
history_df = history_df[keepCols]
history_df = history_df.apply(pd.to_numeric)
matches = history_df.loc[0,'minutes']/90
if matches == 0:
matches = 1
history_df[keepCols] = history_df[keepCols]/matches
history_df.loc[0,'element_code'] = elementCode
playerHistory_df = pd.concat([playerHistory_df,history_df], ignore_index=True)
return playerHistory_df
def ReadPlayerGameweekHistory(path, keepCols):
players = os.listdir(path)
playerHistory_df = pd.DataFrame()
for player in players:
if os.path.isfile(path + player + "/gw.csv"):
historyPath = path + player + "/gw.csv"
history_df = pd.read_csv(historyPath)
history_df = history_df.tail(1)
if not history_df.empty:
history_df = history_df.reset_index()
element = history_df.loc[0,'element']
history_df = history_df[keepCols]
history_df = history_df.apply(pd.to_numeric)
history_df.loc[0,'element'] = element
playerHistory_df = pd.concat([playerHistory_df,history_df], ignore_index=True)
return playerHistory_df
def ReadFixtures(path, round, playerData_df):
fixtures_df = pd.read_csv(path + "fixtures.csv")
fixtures_df = fixtures_df.loc[fixtures_df.event == round]
fixtures_df = fixtures_df.reset_index()
playerData_df['h_team'] = np.nan
playerData_df['a_team'] = np.nan
playerData_df['opponent_team'] = np.nan
playerData_df['was_home'] = np.nan
teamsChecked = ()
for ind in fixtures_df.index:
home = fixtures_df['team_h'][ind]
away = fixtures_df['team_a'][ind]
if home in teamsChecked:
dTeam = playerData_df['player_team'] == home
dTeam_df = playerData_df[dTeam]
dTeam_df.loc[dTeam_df.player_team == home, "opponent_team"] = away
dTeam_df.loc[dTeam_df.player_team == home, "was_home"] = 1
playerData_df = pd.concat([playerData_df,dTeam_df])
else:
playerData_df.loc[playerData_df.player_team == home, "opponent_team"] = away
playerData_df.loc[playerData_df.player_team == home, "was_home"] = 1
teamsChecked = teamsChecked + (home,)
if away in teamsChecked:
dTeam = playerData_df['player_team'] == away
dTeam_df = playerData_df[dTeam]
dTeam_df.loc[dTeam_df.player_team == away, "opponent_team"] = home
dTeam_df.loc[dTeam_df.player_team == away, "was_home"] = 0
playerData_df = pd.concat([playerData_df,dTeam_df])
else:
playerData_df.loc[playerData_df.player_team == away, "opponent_team"] = home
playerData_df.loc[playerData_df.player_team == away, "was_home"] = 0
teamsChecked = teamsChecked + (away,)
return playerData_df